另一个工作是佛罗里达大学的大规模计算资源,GatorTron[2],从头开始开发了一个LLM(没有基于其他预训练模型),使用89亿个参数和来自电子健康记录的>900亿字的文本来改进5个临床自然语言处理任务,包括医疗问题回答和医疗关系提取。虽然比Med-PaLM的模型小得多,但这是第一个由学术医疗机构开发的医学基础模型,而不是像谷...
LLMs augmented with tools, and LMMs). The best-performing model, Multimodal Bard, achieves only 58\% of human performance (34.8\% vs 60.3\%), indicating ample room for further improvement. Given this significant gap, MathVista fuels future research ...
Operationalizing AI and scaling AI is a complex challenge, and especially so for LLMs. The challenges that data science teams typically face in enterprise settings—siloed work, long development cycles, model accuracy, scalability, real time data, and so on—are certainly big issues facing teams ...
Large Language Model. LLMs are AI systems used to model and process human language. Transformers are the underlying technologies behind LLMs. They are called “large” because they have hundreds of millions or even billions of parameters, which are pre-trained using a massive corpus of text dat...
For example, an organization could use one of these platforms to take a model from Hugging Face,train the model on its proprietary dataand useprompt engineeringto fine-tune the model.Hugging Faceis an open source repository of many LLMs, like a GitHub for AI. It provides tools that enable...
Foundation Model: Foundation Model是一种新型的神经网络架构,它旨在作为各种任务和模态的统一架构。它的设计原则支持多模态基础模型的开发,在不牺牲性能的前提下将统一的Transformer用于各种模态。Foundation Model的网络结构旨在保障训练的稳定性,从而降低基础模型大规模预训练的难度。
LLM-based:Theauthors useT5as the base model — by repurposing it for 5 time-series analysis tasks. Lightweight execution:MOMENTwas designed to work with limited resources and time — making it suitable for faster execution. Zero-shot forecasting:MOMENTspecializes in zero-shot scenar...
LLMs:《A Decoder-Only Foundation Model For Time-Series Forecasting》的翻译与解读 导读:本文提出了一种名为TimesFM的时序基础模型,用于零样本学习模式下的时序预测任务。 背景痛点:近年来,深度学习模型在有充足训练数据的情况下已成为时序预测的主流方法,但这些方法通常需要独立在每个数据集上训练。同时,自然语言处...
Training a single generic model for solving arbitrary datasets is always a dream for ML researchers, especially in the era of foundation models. While such dreams have been realized in perception…
“just work” on so many different language reasoning domains, but we’re still not sure exactly how to bring these to bear for robotics. In mid-2022, it seems that we can keep the LLM artifact in a tightly controlled box, providing help with planning and semantic reasoning, but its ...